Update app.py

app.py

@@ -2,53 +2,36 @@ import os
 import torch
 import gradio as gr
 from PIL import Image
-from transformers import AutoModelForCausalLM,AutoProcessor
+from transformers import AutoModelForCausalLM, AutoProcessor
 
 device = 'cuda' if torch.cuda.is_available() else 'cpu'
 
 processor = AutoProcessor.from_pretrained("microsoft/git-base")
 model = AutoModelForCausalLM.from_pretrained("sam749/sd-portrait-caption").to(device)
 
-def generate_captions(images:[Image],max_length=200):
-  # prepare image for the model
-  inputs = processor(images=images, return_tensors="pt").to(device)
-  pixel_values = inputs.pixel_values
-  generated_ids = model.generate(pixel_values=pixel_values, max_length=max_length)
-  generated_caption = processor.batch_decode(generated_ids, skip_special_tokens=True)
-  return generated_caption
+def generate_captions(images, max_length=200):
+    # prepare image for the model
+    inputs = processor(images=images, return_tensors="pt").to(device)
+    pixel_values = inputs.pixel_values
+    generated_ids = model.generate(pixel_values=pixel_values, max_length=max_length)
+    generated_caption = processor.batch_decode(generated_ids, skip_special_tokens=True)
+    return generated_caption
 
-def generate_caption(image,max_length=200):
-  return generate_captions(image,max_length)[0]
+def generate_caption(image, max_length=200):
+    return generate_captions([image], max_length)[0]
 
-
-inputs = [
-    gr.Image(sources=["upload", "clipboard"],
-             height=400,
-             type="pil"
-             ),
-    gr.Slider(minimum=10,
-              maximum=400,
-              value=200,
-              label='max length',
-              step=8,
-              )
-]
-outputs = [
-    gr.Text(label="Generated Caption"),
-]
+image_input = gr.Image(source="upload", type="pil", label="Upload Image", height=400)
+max_length_slider = gr.Slider(minimum=10, maximum=400, value=200, step=8, label="Max Length")
+caption_output = gr.Textbox(label="Generated Caption")
 
 demo = gr.Interface(
     fn=generate_caption,
-    inputs=inputs,
-    outputs=outputs,
+    inputs=[image_input, max_length_slider],
+    outputs=caption_output,
     title="Stable Diffusion Portrait Captioner",
-    theme="gradio/monochrome",
-    api_name="caption",
-    submit_btn=gr.Button("caption it", variant="primary"),
-    allow_flagging="never",
-)
-demo.queue(
-    max_size=10,
+    theme="default",
+    allow_flagging="never"
 )
 
-demo.launch()
+if __name__ == "__main__":
+    demo.launch()